{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 短视频生活场景对消费者购买意愿的影响研究"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1、问卷设计"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "问卷设计分为四个部分：个人信息部分、短视频使用现状部分、短视频生活场景量表部分以及购买意愿量表部分。量表采用李克特5点量表，1代表完全不同意，5代表完全同意，得分越高表示越同意。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2、假设检验"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "基于前期调研和文献回顾，我们提出以下假设：\n",
    "> 假设1：精准匹配性和购买意愿显著正向相关；\n",
    "> 假设2：感知价值和购买意愿显著正向相关；\n",
    "> 假设3：功能价值和购买意愿显著正向相关；"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3、数据收集"
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "本次研究随机选取了多个受访者进行在线问卷调查，通过问卷星平台发放问卷。在收集的问卷中，剔除了无效问卷，最终得到有效问卷，其有效回收率高于90%。"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "导入数据分析所需要的库。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-08-14T10:31:05.308451600Z",
     "start_time": "2024-08-14T10:31:05.199237Z"
    }
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import pingouin as pg\n",
    "import warnings"
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "### 3.1导入数据"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": true,
    "ExecuteTime": {
     "end_time": "2024-08-14T10:31:06.480846200Z",
     "start_time": "2024-08-14T10:31:06.398880300Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "   Q1  Q2  Q3  Q4  Q5.1  Q5.2  Q5.3  Q5.4  Q5.5  Q5.6  ...  Q9.4  Q10.1  \\\n0   1   2   3   5   1.0   1.0   1.0   NaN   NaN   NaN  ...     2      1   \n1   2   3   4   4   NaN   1.0   NaN   NaN   NaN   NaN  ...     1      1   \n2   1   3   4   3   NaN   NaN   NaN   NaN   1.0   NaN  ...     1      2   \n3   2   5   5   4   NaN   NaN   1.0   NaN   NaN   NaN  ...     4      5   \n4   1   2   1   2   NaN   NaN   1.0   NaN   1.0   NaN  ...     3      3   \n\n   Q10.2  Q10.3  Q11.1  Q11.2  Q11.3  Q12.1  Q12.2  Q12.3  \n0      1      1      2      1      2      2      2      2  \n1      2      1      2      2      2      2      2      1  \n2      2      1      2      1      1      2      1      2  \n3      5      3      5      4      3      5      3      4  \n4      3      4      5      5      5      3      3      4  \n\n[5 rows x 26 columns]",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Q1</th>\n      <th>Q2</th>\n      <th>Q3</th>\n      <th>Q4</th>\n      <th>Q5.1</th>\n      <th>Q5.2</th>\n      <th>Q5.3</th>\n      <th>Q5.4</th>\n      <th>Q5.5</th>\n      <th>Q5.6</th>\n      <th>...</th>\n      <th>Q9.4</th>\n      <th>Q10.1</th>\n      <th>Q10.2</th>\n      <th>Q10.3</th>\n      <th>Q11.1</th>\n      <th>Q11.2</th>\n      <th>Q11.3</th>\n      <th>Q12.1</th>\n      <th>Q12.2</th>\n      <th>Q12.3</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1</td>\n      <td>2</td>\n      <td>3</td>\n      <td>5</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>2</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>2</td>\n      <td>1</td>\n      <td>2</td>\n      <td>2</td>\n      <td>2</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2</td>\n      <td>3</td>\n      <td>4</td>\n      <td>4</td>\n      <td>NaN</td>\n      <td>1.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>1</td>\n      <td>1</td>\n      <td>2</td>\n      <td>1</td>\n      <td>2</td>\n      <td>2</td>\n      <td>2</td>\n      <td>2</td>\n      <td>2</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>1</td>\n      <td>3</td>\n      <td>4</td>\n      <td>3</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>1.0</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>1</td>\n      <td>2</td>\n      <td>2</td>\n      <td>1</td>\n      <td>2</td>\n      <td>1</td>\n      <td>1</td>\n      <td>2</td>\n      <td>1</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>2</td>\n      <td>5</td>\n      <td>5</td>\n      <td>4</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>1.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>4</td>\n      <td>5</td>\n      <td>5</td>\n      <td>3</td>\n      <td>5</td>\n      <td>4</td>\n      <td>3</td>\n      <td>5</td>\n      <td>3</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>1</td>\n      <td>2</td>\n      <td>1</td>\n      <td>2</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>1.0</td>\n      <td>NaN</td>\n      <td>1.0</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>3</td>\n      <td>3</td>\n      <td>3</td>\n      <td>4</td>\n      <td>5</td>\n      <td>5</td>\n      <td>5</td>\n      <td>3</td>\n      <td>3</td>\n      <td>4</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 26 columns</p>\n</div>"
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "original_Shortvideo_data = pd.read_csv(\"Shortvideo_data.csv\")\n",
    "original_Shortvideo_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.2评估和清理数据"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "发现Q5为多选题，我们需要将这些字符串转换为独立的列，每一列对应一个可能的选择。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 238 entries, 0 to 237\n",
      "Data columns (total 26 columns):\n",
      " #   Column  Non-Null Count  Dtype  \n",
      "---  ------  --------------  -----  \n",
      " 0   Q1      238 non-null    int64  \n",
      " 1   Q2      238 non-null    int64  \n",
      " 2   Q3      238 non-null    int64  \n",
      " 3   Q4      238 non-null    int64  \n",
      " 4   Q5.1    82 non-null     float64\n",
      " 5   Q5.2    85 non-null     float64\n",
      " 6   Q5.3    73 non-null     float64\n",
      " 7   Q5.4    73 non-null     float64\n",
      " 8   Q5.5    79 non-null     float64\n",
      " 9   Q5.6    62 non-null     float64\n",
      " 10  Q6      238 non-null    int64  \n",
      " 11  Q7      238 non-null    int64  \n",
      " 12  Q8      238 non-null    int64  \n",
      " 13  Q9.1    238 non-null    int64  \n",
      " 14  Q9.2    238 non-null    int64  \n",
      " 15  Q9.3    238 non-null    int64  \n",
      " 16  Q9.4    238 non-null    int64  \n",
      " 17  Q10.1   238 non-null    int64  \n",
      " 18  Q10.2   238 non-null    int64  \n",
      " 19  Q10.3   238 non-null    int64  \n",
      " 20  Q11.1   238 non-null    int64  \n",
      " 21  Q11.2   238 non-null    int64  \n",
      " 22  Q11.3   238 non-null    int64  \n",
      " 23  Q12.1   238 non-null    int64  \n",
      " 24  Q12.2   238 non-null    int64  \n",
      " 25  Q12.3   238 non-null    int64  \n",
      "dtypes: float64(6), int64(20)\n",
      "memory usage: 48.5 KB\n"
     ]
    }
   ],
   "source": [
    "original_Shortvideo_data.info()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-08-14T10:31:08.102314400Z",
     "start_time": "2024-08-14T10:31:08.076762Z"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "从输出结果来看，`original_Shortvideo_data`共有238条观察值，其中`Q5`存在缺失值，将在后续进行评估和清理。"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "因为空值都是不使用相关短视频APP的，所以填充为0即可"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "outputs": [
    {
     "data": {
      "text/plain": "     Q1  Q2  Q3  Q4  Q5.1  Q5.2  Q5.3  Q5.4  Q5.5  Q5.6  ...  Q9.4  Q10.1  \\\n0     1   2   3   5     1     1     1     0     0     0  ...     2      1   \n1     2   3   4   4     0     1     0     0     0     0  ...     1      1   \n2     1   3   4   3     0     0     0     0     1     0  ...     1      2   \n3     2   5   5   4     0     0     1     0     0     0  ...     4      5   \n4     1   2   1   2     0     0     1     0     1     0  ...     3      3   \n..   ..  ..  ..  ..   ...   ...   ...   ...   ...   ...  ...   ...    ...   \n233   1   2   3   3     0     0     0     0     1     0  ...     3      4   \n234   2   3   4   4     0     1     0     0     0     0  ...     4      3   \n235   2   2   2   3     1     0     0     0     0     0  ...     2      1   \n236   2   5   5   4     0     0     0     0     0     1  ...     4      3   \n237   1   4   3   5     0     0     1     0     0     0  ...     3      3   \n\n     Q10.2  Q10.3  Q11.1  Q11.2  Q11.3  Q12.1  Q12.2  Q12.3  \n0        1      1      2      1      2      2      2      2  \n1        2      1      2      2      2      2      2      1  \n2        2      1      2      1      1      2      1      2  \n3        5      3      5      4      3      5      3      4  \n4        3      4      5      5      5      3      3      4  \n..     ...    ...    ...    ...    ...    ...    ...    ...  \n233      3      3      3      3      4      5      3      5  \n234      3      5      5      3      5      4      5      3  \n235      1      1      1      1      2      2      1      2  \n236      3      5      4      4      4      3      4      4  \n237      5      3      3      3      3      4      5      5  \n\n[238 rows x 26 columns]",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Q1</th>\n      <th>Q2</th>\n      <th>Q3</th>\n      <th>Q4</th>\n      <th>Q5.1</th>\n      <th>Q5.2</th>\n      <th>Q5.3</th>\n      <th>Q5.4</th>\n      <th>Q5.5</th>\n      <th>Q5.6</th>\n      <th>...</th>\n      <th>Q9.4</th>\n      <th>Q10.1</th>\n      <th>Q10.2</th>\n      <th>Q10.3</th>\n      <th>Q11.1</th>\n      <th>Q11.2</th>\n      <th>Q11.3</th>\n      <th>Q12.1</th>\n      <th>Q12.2</th>\n      <th>Q12.3</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1</td>\n      <td>2</td>\n      <td>3</td>\n      <td>5</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>2</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>2</td>\n      <td>1</td>\n      <td>2</td>\n      <td>2</td>\n      <td>2</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2</td>\n      <td>3</td>\n      <td>4</td>\n      <td>4</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>1</td>\n      <td>1</td>\n      <td>2</td>\n      <td>1</td>\n      <td>2</td>\n      <td>2</td>\n      <td>2</td>\n      <td>2</td>\n      <td>2</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>1</td>\n      <td>3</td>\n      <td>4</td>\n      <td>3</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>...</td>\n      <td>1</td>\n      <td>2</td>\n      <td>2</td>\n      <td>1</td>\n      <td>2</td>\n      <td>1</td>\n      <td>1</td>\n      <td>2</td>\n      <td>1</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>2</td>\n      <td>5</td>\n      <td>5</td>\n      <td>4</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>4</td>\n      <td>5</td>\n      <td>5</td>\n      <td>3</td>\n      <td>5</td>\n      <td>4</td>\n      <td>3</td>\n      <td>5</td>\n      <td>3</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>1</td>\n      <td>2</td>\n      <td>1</td>\n      <td>2</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>...</td>\n      <td>3</td>\n      <td>3</td>\n      <td>3</td>\n      <td>4</td>\n      <td>5</td>\n      <td>5</td>\n      <td>5</td>\n      <td>3</td>\n      <td>3</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>233</th>\n      <td>1</td>\n      <td>2</td>\n      <td>3</td>\n      <td>3</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>...</td>\n      <td>3</td>\n      <td>4</td>\n      <td>3</td>\n      <td>3</td>\n      <td>3</td>\n      <td>3</td>\n      <td>4</td>\n      <td>5</td>\n      <td>3</td>\n      <td>5</td>\n    </tr>\n    <tr>\n      <th>234</th>\n      <td>2</td>\n      <td>3</td>\n      <td>4</td>\n      <td>4</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>4</td>\n      <td>3</td>\n      <td>3</td>\n      <td>5</td>\n      <td>5</td>\n      <td>3</td>\n      <td>5</td>\n      <td>4</td>\n      <td>5</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>235</th>\n      <td>2</td>\n      <td>2</td>\n      <td>2</td>\n      <td>3</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>2</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>2</td>\n      <td>2</td>\n      <td>1</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>236</th>\n      <td>2</td>\n      <td>5</td>\n      <td>5</td>\n      <td>4</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>...</td>\n      <td>4</td>\n      <td>3</td>\n      <td>3</td>\n      <td>5</td>\n      <td>4</td>\n      <td>4</td>\n      <td>4</td>\n      <td>3</td>\n      <td>4</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>237</th>\n      <td>1</td>\n      <td>4</td>\n      <td>3</td>\n      <td>5</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>3</td>\n      <td>3</td>\n      <td>5</td>\n      <td>3</td>\n      <td>3</td>\n      <td>3</td>\n      <td>3</td>\n      <td>4</td>\n      <td>5</td>\n      <td>5</td>\n    </tr>\n  </tbody>\n</table>\n<p>238 rows × 26 columns</p>\n</div>"
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "original_Shortvideo_data.fillna(0, inplace=True)\n",
    "original_Shortvideo_data = original_Shortvideo_data.astype(float).astype(int)\n",
    "original_Shortvideo_data"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-08-14T10:31:09.717316900Z",
     "start_time": "2024-08-14T10:31:09.676715800Z"
    }
   }
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "为了区分开经过清理的数据和原始的数据，我们创建新的变量`cleaned_Shortvideo_data`，让它为`original_Shortvideo_data`复制出的副本。我们之后的清理步骤都将被运用在`cleaned_Shortvideo_data`上，赋予列命，处理问题更直观"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-08-14T10:31:11.119051100Z",
     "start_time": "2024-08-14T10:31:11.058174700Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "     Sex  Age  Vocation  Monthly_income  APP1  APP2  APP3  APP4  APP5  APP6  \\\n0      1    2         3               5     1     1     1     0     0     0   \n1      2    3         4               4     0     1     0     0     0     0   \n2      1    3         4               3     0     0     0     0     1     0   \n3      2    5         5               4     0     0     1     0     0     0   \n4      1    2         1               2     0     0     1     0     1     0   \n..   ...  ...       ...             ...   ...   ...   ...   ...   ...   ...   \n233    1    2         3               3     0     0     0     0     1     0   \n234    2    3         4               4     0     1     0     0     0     0   \n235    2    2         2               3     1     0     0     0     0     0   \n236    2    5         5               4     0     0     0     0     0     1   \n237    1    4         3               5     0     0     1     0     0     0   \n\n     ...  Need  Curiosity  Feel  Release_pressure  understand  Decision  Risk  \\\n0    ...     2          1     1                 1           2         1     2   \n1    ...     1          1     2                 1           2         2     2   \n2    ...     1          2     2                 1           2         1     1   \n3    ...     4          5     5                 3           5         4     3   \n4    ...     3          3     3                 4           5         5     5   \n..   ...   ...        ...   ...               ...         ...       ...   ...   \n233  ...     3          4     3                 3           3         3     4   \n234  ...     4          3     3                 5           5         3     5   \n235  ...     2          1     1                 1           1         1     2   \n236  ...     4          3     3                 5           4         4     4   \n237  ...     3          3     5                 3           3         3     3   \n\n     Possibility  Worth  Recommend  \n0              2      2          2  \n1              2      2          1  \n2              2      1          2  \n3              5      3          4  \n4              3      3          4  \n..           ...    ...        ...  \n233            5      3          5  \n234            4      5          3  \n235            2      1          2  \n236            3      4          4  \n237            4      5          5  \n\n[238 rows x 26 columns]",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Sex</th>\n      <th>Age</th>\n      <th>Vocation</th>\n      <th>Monthly_income</th>\n      <th>APP1</th>\n      <th>APP2</th>\n      <th>APP3</th>\n      <th>APP4</th>\n      <th>APP5</th>\n      <th>APP6</th>\n      <th>...</th>\n      <th>Need</th>\n      <th>Curiosity</th>\n      <th>Feel</th>\n      <th>Release_pressure</th>\n      <th>understand</th>\n      <th>Decision</th>\n      <th>Risk</th>\n      <th>Possibility</th>\n      <th>Worth</th>\n      <th>Recommend</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1</td>\n      <td>2</td>\n      <td>3</td>\n      <td>5</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>2</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>2</td>\n      <td>1</td>\n      <td>2</td>\n      <td>2</td>\n      <td>2</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2</td>\n      <td>3</td>\n      <td>4</td>\n      <td>4</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>1</td>\n      <td>1</td>\n      <td>2</td>\n      <td>1</td>\n      <td>2</td>\n      <td>2</td>\n      <td>2</td>\n      <td>2</td>\n      <td>2</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>1</td>\n      <td>3</td>\n      <td>4</td>\n      <td>3</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>...</td>\n      <td>1</td>\n      <td>2</td>\n      <td>2</td>\n      <td>1</td>\n      <td>2</td>\n      <td>1</td>\n      <td>1</td>\n      <td>2</td>\n      <td>1</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>2</td>\n      <td>5</td>\n      <td>5</td>\n      <td>4</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>4</td>\n      <td>5</td>\n      <td>5</td>\n      <td>3</td>\n      <td>5</td>\n      <td>4</td>\n      <td>3</td>\n      <td>5</td>\n      <td>3</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>1</td>\n      <td>2</td>\n      <td>1</td>\n      <td>2</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>...</td>\n      <td>3</td>\n      <td>3</td>\n      <td>3</td>\n      <td>4</td>\n      <td>5</td>\n      <td>5</td>\n      <td>5</td>\n      <td>3</td>\n      <td>3</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>233</th>\n      <td>1</td>\n      <td>2</td>\n      <td>3</td>\n      <td>3</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>...</td>\n      <td>3</td>\n      <td>4</td>\n      <td>3</td>\n      <td>3</td>\n      <td>3</td>\n      <td>3</td>\n      <td>4</td>\n      <td>5</td>\n      <td>3</td>\n      <td>5</td>\n    </tr>\n    <tr>\n      <th>234</th>\n      <td>2</td>\n      <td>3</td>\n      <td>4</td>\n      <td>4</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>4</td>\n      <td>3</td>\n      <td>3</td>\n      <td>5</td>\n      <td>5</td>\n      <td>3</td>\n      <td>5</td>\n      <td>4</td>\n      <td>5</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>235</th>\n      <td>2</td>\n      <td>2</td>\n      <td>2</td>\n      <td>3</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>2</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>2</td>\n      <td>2</td>\n      <td>1</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>236</th>\n      <td>2</td>\n      <td>5</td>\n      <td>5</td>\n      <td>4</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>...</td>\n      <td>4</td>\n      <td>3</td>\n      <td>3</td>\n      <td>5</td>\n      <td>4</td>\n      <td>4</td>\n      <td>4</td>\n      <td>3</td>\n      <td>4</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>237</th>\n      <td>1</td>\n      <td>4</td>\n      <td>3</td>\n      <td>5</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>3</td>\n      <td>3</td>\n      <td>5</td>\n      <td>3</td>\n      <td>3</td>\n      <td>3</td>\n      <td>3</td>\n      <td>4</td>\n      <td>5</td>\n      <td>5</td>\n    </tr>\n  </tbody>\n</table>\n<p>238 rows × 26 columns</p>\n</div>"
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cleaned_Shortvideo_data = original_Shortvideo_data.copy()\n",
    "cleaned_Shortvideo_data.columns = ['Sex', 'Age', 'Vocation', 'Monthly_income', 'APP1', 'APP2', 'APP3', 'APP4', 'APP5',\n",
    "                                   'APP6', 'Frequency', 'Time', 'Duration', 'Hobby', 'Fit', 'Customized', 'Need',\n",
    "                                   'Curiosity', 'Feel', 'Release_pressure', 'understand', 'Decision', 'Risk',\n",
    "                                   'Possibility', 'Worth', 'Recommend', ]\n",
    "cleaned_Shortvideo_data"
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "## 4信度与效度检验\n",
    "### 4.1信度检验\n",
    "对问卷进行Cronbach’s Alpha信度检验。如果此值高于0.8，则说明信度高；如果此值介于0.7~0.8之间，则说明信度较好；如果此值介于0.6~0.7，则说明信度可接受；如果此值小于0.6，说明信度不佳。\n",
    "对问卷进行Cronbach’s Alpha信度检验。统计结果显示，各维度的Cronbach’s α系数如下：精准匹配性因素的Cronbach’s α系数为0.861，感知价值因素的Cronbach’s α系数为0.833，功能价值因素的Cronbach’s α系数为0.812，购买意愿因素的Cronbach’s α系数为0.818。问卷各量表均大于0.7，说明数据信度质量高，可用于进一步分析。"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cronbach's Alpha for '精准匹配性': 0.861\n",
      "Cronbach's Alpha for '感知价值': 0.833\n",
      "Cronbach's Alpha for '功能价值': 0.812\n",
      "Cronbach's Alpha for '购买意愿': 0.818\n"
     ]
    }
   ],
   "source": [
    "# 定义各个组的列名\n",
    "groups = {\n",
    "    '精准匹配性': ['Hobby', 'Fit', 'Customized', 'Need'],\n",
    "    '感知价值': ['Curiosity', 'Feel', 'Release_pressure'],\n",
    "    '功能价值': ['understand', 'Decision', 'Risk'],\n",
    "    '购买意愿': ['Possibility', 'Worth', 'Recommend']\n",
    "}\n",
    "\n",
    "# 对每一组进行Cronbach's Alpha计算\n",
    "for group_name, columns in groups.items():\n",
    "    data_group = cleaned_Shortvideo_data[columns]\n",
    "    alpha_result = pg.cronbach_alpha(data_group)\n",
    "\n",
    "    if isinstance(alpha_result, dict):\n",
    "        alpha_value = alpha_result['Cronbach Alpha']\n",
    "    elif isinstance(alpha_result, (tuple, list)):\n",
    "        alpha_value = alpha_result[0]\n",
    "    else:\n",
    "        raise TypeError(\"Unexpected return type from cronbach_alpha\")\n",
    "\n",
    "    print(f\"Cronbach's Alpha for '{group_name}': {alpha_value:.3f}\")\n",
    "\n"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-08-14T10:31:12.771544100Z",
     "start_time": "2024-08-14T10:31:12.751225200Z"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "### 4.2效度检验\n",
    "对量表的效度分析采用KMO和Bartlett球检验法进行检验。如果此值高于0.8，则说明研究数据非常适合提取信息，效度很好；如果此值介于0.7-0.8之间，则说明研究数据适合提取信息，效度较好；如果此值介于0.6-0.7，则说明研究数据比较适合提取信息，效度一般；如果此值小于0.6，说明数据效度一般。\n",
    "对量表的效度分析采用KMO和Bartlett球检验法进行检验，检验结果所示。各织支持感的KMO结果结果为0.8913085528437634，大于0.6，Bartlett的球形度检验中p=9.255447864372839e-257<0.001，说明变量间存在相关因子，适合做因子分析。"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "outputs": [
    {
     "data": {
      "text/plain": "     Hobby  Fit  Customized  Need  Curiosity  Feel  Release_pressure  \\\n0        2    1           1     2          1     1                 1   \n1        1    1           2     1          1     2                 1   \n2        1    2           2     1          2     2                 1   \n3        3    3           5     4          5     5                 3   \n4        3    3           4     3          3     3                 4   \n..     ...  ...         ...   ...        ...   ...               ...   \n233      5    5           3     3          4     3                 3   \n234      3    5           4     4          3     3                 5   \n235      2    1           1     2          1     1                 1   \n236      3    4           5     4          3     3                 5   \n237      4    4           4     3          3     5                 3   \n\n     understand  Decision  Risk  Possibility  Worth  Recommend  \n0             2         1     2            2      2          2  \n1             2         2     2            2      2          1  \n2             2         1     1            2      1          2  \n3             5         4     3            5      3          4  \n4             5         5     5            3      3          4  \n..          ...       ...   ...          ...    ...        ...  \n233           3         3     4            5      3          5  \n234           5         3     5            4      5          3  \n235           1         1     2            2      1          2  \n236           4         4     4            3      4          4  \n237           3         3     3            4      5          5  \n\n[238 rows x 13 columns]",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Hobby</th>\n      <th>Fit</th>\n      <th>Customized</th>\n      <th>Need</th>\n      <th>Curiosity</th>\n      <th>Feel</th>\n      <th>Release_pressure</th>\n      <th>understand</th>\n      <th>Decision</th>\n      <th>Risk</th>\n      <th>Possibility</th>\n      <th>Worth</th>\n      <th>Recommend</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>2</td>\n      <td>1</td>\n      <td>1</td>\n      <td>2</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>2</td>\n      <td>1</td>\n      <td>2</td>\n      <td>2</td>\n      <td>2</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1</td>\n      <td>1</td>\n      <td>2</td>\n      <td>1</td>\n      <td>1</td>\n      <td>2</td>\n      <td>1</td>\n      <td>2</td>\n      <td>2</td>\n      <td>2</td>\n      <td>2</td>\n      <td>2</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>1</td>\n      <td>2</td>\n      <td>2</td>\n      <td>1</td>\n      <td>2</td>\n      <td>2</td>\n      <td>1</td>\n      <td>2</td>\n      <td>1</td>\n      <td>1</td>\n      <td>2</td>\n      <td>1</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>3</td>\n      <td>3</td>\n      <td>5</td>\n      <td>4</td>\n      <td>5</td>\n      <td>5</td>\n      <td>3</td>\n      <td>5</td>\n      <td>4</td>\n      <td>3</td>\n      <td>5</td>\n      <td>3</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>3</td>\n      <td>3</td>\n      <td>4</td>\n      <td>3</td>\n      <td>3</td>\n      <td>3</td>\n      <td>4</td>\n      <td>5</td>\n      <td>5</td>\n      <td>5</td>\n      <td>3</td>\n      <td>3</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>233</th>\n      <td>5</td>\n      <td>5</td>\n      <td>3</td>\n      <td>3</td>\n      <td>4</td>\n      <td>3</td>\n      <td>3</td>\n      <td>3</td>\n      <td>3</td>\n      <td>4</td>\n      <td>5</td>\n      <td>3</td>\n      <td>5</td>\n    </tr>\n    <tr>\n      <th>234</th>\n      <td>3</td>\n      <td>5</td>\n      <td>4</td>\n      <td>4</td>\n      <td>3</td>\n      <td>3</td>\n      <td>5</td>\n      <td>5</td>\n      <td>3</td>\n      <td>5</td>\n      <td>4</td>\n      <td>5</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>235</th>\n      <td>2</td>\n      <td>1</td>\n      <td>1</td>\n      <td>2</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>2</td>\n      <td>2</td>\n      <td>1</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>236</th>\n      <td>3</td>\n      <td>4</td>\n      <td>5</td>\n      <td>4</td>\n      <td>3</td>\n      <td>3</td>\n      <td>5</td>\n      <td>4</td>\n      <td>4</td>\n      <td>4</td>\n      <td>3</td>\n      <td>4</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>237</th>\n      <td>4</td>\n      <td>4</td>\n      <td>4</td>\n      <td>3</td>\n      <td>3</td>\n      <td>5</td>\n      <td>3</td>\n      <td>3</td>\n      <td>3</td>\n      <td>3</td>\n      <td>4</td>\n      <td>5</td>\n      <td>5</td>\n    </tr>\n  </tbody>\n</table>\n<p>238 rows × 13 columns</p>\n</div>"
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 定义分组\n",
    "groups = {\n",
    "    '精准匹配性': ['Hobby', 'Fit', 'Customized', 'Need'],\n",
    "    '感知价值': ['Curiosity', 'Feel', 'Release_pressure'],\n",
    "    '功能价值': ['understand', 'Decision', 'Risk'],\n",
    "    '购买意愿': ['Possibility', 'Worth', 'Recommend']\n",
    "}\n",
    "\n",
    "# 创建新表\n",
    "newcleaned_Shortvideo_data = pd.concat([cleaned_Shortvideo_data[group] for group in groups.values()], axis=1)\n",
    "\n",
    "# 输出新表\n",
    "newcleaned_Shortvideo_data"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-08-14T10:31:14.259913900Z",
     "start_time": "2024-08-14T10:31:14.231833100Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Bartlett's Test of Sphericity: Chi-Square=1475.1009797696036/238\n",
      "p-value=9.255447864372839e-257\n",
      "Kaiser-Meyer-Olkin Measure of Sampling Adequacy: Overall=0.8913085528437634\n",
      "Degrees of Freedom for Bartlett's Test: 65\n"
     ]
    }
   ],
   "source": [
    "from factor_analyzer.factor_analyzer import calculate_bartlett_sphericity, calculate_kmo\n",
    "\n",
    "# 假设所有列都是连续变量，准备进行因子分析\n",
    "KMOdata = newcleaned_Shortvideo_data.select_dtypes(include=[np.number])\n",
    "\n",
    "# 执行Bartlett's球形检验\n",
    "chi_square_value, p_value = calculate_bartlett_sphericity(KMOdata)\n",
    "print(\n",
    "    f\"Bartlett's Test of Sphericity: Chi-Square={chi_square_value}/{newcleaned_Shortvideo_data.shape[0]}\\np-value={p_value}\")\n",
    "\n",
    "# 如果p值小于0.05，那么拒绝零假设，认为数据适合做因子分析\n",
    "\n",
    "# 计算KMO\n",
    "kmo_all, kmo_model = calculate_kmo(KMOdata)\n",
    "print(f\"Kaiser-Meyer-Olkin Measure of Sampling Adequacy: Overall={kmo_model}\")\n",
    "# 变量数量\n",
    "N = KMOdata.shape[1]\n",
    "\n",
    "# 自由度计算\n",
    "df = int((N * (N - 1)) / 2 - N)\n",
    "\n",
    "print(f\"Degrees of Freedom for Bartlett's Test: {df}\")\n",
    "# 如果KMO值大于0.6，那么数据适合做因子分析"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-08-14T10:31:15.129448800Z",
     "start_time": "2024-08-14T10:31:15.047054500Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 7.12750854e-01 -4.47832781e-01 -1.12975141e-01 -7.76241823e-02\n",
      "   1.00538971e-01 -2.90678546e-02  4.81061338e-02 -2.65986063e-01\n",
      "  -3.16065202e-01 -1.06169349e-01 -1.77125103e-01 -1.56552299e-01\n",
      "   1.46888551e-01]\n",
      " [ 6.75048419e-01 -4.72853783e-01 -2.04497791e-03  2.24293102e-03\n",
      "   3.63480341e-01  8.92411988e-02 -1.40937819e-01  2.36904075e-01\n",
      "   2.12898433e-01  1.04959963e-01 -1.98752713e-01 -2.87841823e-02\n",
      "  -8.92594737e-02]\n",
      " [ 6.92161235e-01 -4.70692676e-01 -1.17895789e-01 -1.26346786e-01\n",
      "  -2.16967394e-01  2.32305944e-01  4.39487555e-02  2.30624220e-02\n",
      "   2.28316780e-02 -2.64547098e-01  1.05504878e-01  2.68142388e-01\n",
      "  -1.11606271e-01]\n",
      " [ 6.75560974e-01 -4.62001110e-01 -1.47237588e-01 -1.33207195e-01\n",
      "  -2.65169489e-01 -2.70213369e-01  2.43839492e-02 -1.55848646e-02\n",
      "   1.12668263e-01  2.77316228e-01  2.10520033e-01 -1.09013917e-01\n",
      "   2.79382728e-02]\n",
      " [ 6.61003916e-01  3.34730940e-01 -2.42578492e-01 -3.56416632e-01\n",
      "   3.27902587e-01 -1.51780320e-01  5.69218217e-02 -1.25142458e-01\n",
      "   1.19578414e-01  6.77359580e-03  9.41728769e-02  2.43970200e-01\n",
      "   1.81545896e-01]\n",
      " [ 6.67711273e-01  3.50264587e-01 -1.03582878e-01 -4.39216852e-01\n",
      "   2.40012752e-03  1.04191787e-01 -1.23630928e-01  2.78182013e-01\n",
      "  -1.16637251e-01 -1.39833859e-01  1.49647372e-01 -2.62035170e-01\n",
      "   9.45881476e-03]\n",
      " [ 6.72527718e-01  4.28217151e-01 -1.71306054e-01 -3.09532964e-01\n",
      "  -2.47881209e-01  5.32633835e-02  1.01532941e-01 -1.03913904e-01\n",
      "   4.03587550e-02  1.39547930e-01 -3.04824664e-01  1.59488474e-02\n",
      "  -1.98786581e-01]\n",
      " [ 6.88527360e-01  1.76710340e-01 -1.88552951e-01  3.94263086e-01\n",
      "  -9.70099938e-02 -2.54619594e-01 -3.24469179e-01  1.66004114e-01\n",
      "  -2.43750029e-01  2.17920901e-02 -6.05761492e-02  1.81850467e-01\n",
      "   4.57907983e-04]\n",
      " [ 6.50351120e-01  1.65516076e-01 -2.62360690e-01  4.39142526e-01\n",
      "   8.48560940e-02  3.86152186e-01  2.23842220e-01  1.54858146e-02\n",
      "  -1.18630032e-01  2.19765682e-01  1.31247819e-01 -7.71803299e-03\n",
      "   4.06662532e-02]\n",
      " [ 6.68887402e-01  2.08677398e-01 -2.40685389e-01  4.74172883e-01\n",
      "  -1.32870366e-02 -1.10617500e-01 -3.27961545e-03 -1.49561341e-01\n",
      "   3.04482811e-01 -2.49812712e-01  1.58115068e-02 -1.84175893e-01\n",
      "  -4.68502273e-02]\n",
      " [ 6.52863968e-01  9.02371850e-02  5.85659799e-01  5.64298701e-03\n",
      "   1.88555170e-01 -4.10242284e-02 -9.71930597e-02 -2.17709228e-01\n",
      "  -1.10929040e-01  4.44246381e-02  1.73312477e-01 -1.27057974e-03\n",
      "  -2.90170947e-01]\n",
      " [ 6.38233909e-01  1.10931756e-01  5.69116103e-01  2.91131161e-02\n",
      "  -1.99375393e-01  2.21397001e-01 -2.35813301e-01 -7.79722340e-02\n",
      "   1.45149601e-01  4.36062841e-02 -5.47782844e-02  1.66425043e-02\n",
      "   2.80906748e-01]\n",
      " [ 6.46643622e-01  5.34566366e-02  5.00349467e-01  1.11028347e-01\n",
      "  -2.27473368e-02 -2.15244421e-01  4.40090399e-01  2.45350279e-01\n",
      "  -2.71640628e-02 -8.04788033e-02 -6.86802323e-02  2.35753802e-02\n",
      "   5.57325216e-02]]\n"
     ]
    }
   ],
   "source": [
    "from factor_analyzer import FactorAnalyzer, calculate_kmo, calculate_bartlett_sphericity\n",
    "\n",
    "Load_Matrix = FactorAnalyzer(rotation=None, n_factors=len(newcleaned_Shortvideo_data.T), method='principal')\n",
    "\n",
    "Load_Matrix.fit(newcleaned_Shortvideo_data)\n",
    "\n",
    "f_contribution_var = Load_Matrix.get_factor_variance()\n",
    "\n",
    "matrices_var = pd.DataFrame()\n",
    "\n",
    "matrices_var[\"旋转前特征值\"] = f_contribution_var[0]\n",
    "\n",
    "matrices_var[\"旋转前方差贡献率\"] = f_contribution_var[1]\n",
    "\n",
    "matrices_var[\"旋转前方差累计贡献率\"] = f_contribution_var[2]\n",
    "\n",
    "matrices_var\n",
    "\n",
    "print(Load_Matrix.loadings_)  #旋转前的成分矩阵"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-08-14T10:31:36.347606100Z",
     "start_time": "2024-08-14T10:31:36.265122500Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "选择了4个因子累计贡献率为0.7309043433501254\n",
      "\n"
     ]
    }
   ],
   "source": [
    "eigenvalues = 1\n",
    "N = 0\n",
    "for c in matrices_var[\"旋转前特征值\"]:\n",
    "    if c >= eigenvalues:\n",
    "        N += 1\n",
    "    else:\n",
    "        s = matrices_var[\"旋转前方差累计贡献率\"][N - 1]\n",
    "        print(\"\\n选择了\" + str(N) + \"个因子累计贡献率为\" + str(s) + \"\\n\")\n",
    "        break"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-08-14T10:31:41.923489500Z",
     "start_time": "2024-08-14T10:31:41.919490400Z"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "相关矩阵特征值： [5.83039072 1.40271612 1.22682032 1.0418293  0.52119579 0.49573109\n",
      " 0.46766768 0.39533036 0.38827894 0.32978113 0.30940716 0.30544409\n",
      " 0.28540728]\n"
     ]
    },
    {
     "data": {
      "text/plain": "<Figure size 800x650 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 主要用来看取多少因子合适，一般是取到平滑处左右，当然还要需要结合贡献率\n",
    "\n",
    "import matplotlib\n",
    "\n",
    "matplotlib.rcParams[\"font.family\"] = \"SimHei\"\n",
    "\n",
    "ev, v = Load_Matrix.get_eigenvalues()\n",
    "\n",
    "print('\\n相关矩阵特征值：', ev)\n",
    "\n",
    "plt.figure(figsize=(8, 6.5))\n",
    "\n",
    "plt.scatter(range(1, newcleaned_Shortvideo_data.shape[1] + 1), ev)\n",
    "\n",
    "plt.plot(range(1, newcleaned_Shortvideo_data.shape[1] + 1), ev)\n",
    "\n",
    "plt.title('特征值和因子个数的变化', fontdict={'weight': 'normal', 'size': 25})\n",
    "\n",
    "plt.xlabel('因子', fontdict={'weight': 'normal', 'size': 15})\n",
    "\n",
    "plt.ylabel('特征值', fontdict={'weight': 'normal', 'size': 15})\n",
    "\n",
    "plt.grid()\n",
    "\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-08-14T10:32:00.251248100Z",
     "start_time": "2024-08-14T10:32:00.121817200Z"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "上图也叫碎石图，从图中我们也可以看出应该选择四个公共因子。"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Original Data Columns: 13\n",
      "Original Data Columns Names: ['Hobby', 'Fit', 'Customized', 'Need', 'Curiosity', 'Feel', 'Release_pressure', 'understand', 'Decision', 'Risk', 'Possibility', 'Worth', 'Recommend']\n",
      "Factor Loadings:\n",
      "                  Factor_1  Factor_2  Factor_3  Factor_4\n",
      "Hobby             0.731856  0.182038  0.191339  0.208488\n",
      "Fit               0.668946  0.096858  0.258778  0.204664\n",
      "Customized        0.742221  0.192258  0.168265  0.164930\n",
      "Need              0.713125  0.196734  0.143983  0.174227\n",
      "Curiosity         0.223655  0.670020  0.145760  0.249969\n",
      "Feel              0.205554  0.735879  0.236249  0.148241\n",
      "Release_pressure  0.147936  0.720463  0.202145  0.276129\n",
      "understand        0.243137  0.248697  0.224759  0.623041\n",
      "Decision          0.237743  0.226382  0.160965  0.631834\n",
      "Risk              0.192309  0.205642  0.176218  0.773397\n",
      "Possibility       0.201444  0.193812  0.783137  0.138430\n",
      "Worth             0.190325  0.200590  0.704008  0.167049\n",
      "Recommend         0.238558  0.162773  0.620190  0.228126\n",
      "\n",
      "Communalities:\n",
      "                  Communalities\n",
      "Hobby                  0.648828\n",
      "Fit                    0.565723\n",
      "Customized             0.643371\n",
      "Need                   0.598338\n",
      "Curiosity              0.582679\n",
      "Feel                   0.661560\n",
      "Release_pressure       0.658062\n",
      "understand             0.559662\n",
      "Decision               0.532894\n",
      "Risk                   0.708467\n",
      "Possibility            0.710609\n",
      "Worth                  0.599993\n",
      "Recommend              0.520082\n"
     ]
    }
   ],
   "source": [
    "from factor_analyzer import FactorAnalyzer\n",
    "\n",
    "# 假设 'newcleaned_Shortvideo_data' 已经是你的 DataFrame 变量\n",
    "# 我们将使用 select_dtypes 方法仅选择数值型列进行因子分析\n",
    "factor_data = newcleaned_Shortvideo_data.select_dtypes(include=[np.number])\n",
    "\n",
    "# 输出原始数据的列数和列名，用于调试\n",
    "print(\"Original Data Columns:\", factor_data.shape[1])\n",
    "print(\"Original Data Columns Names:\", list(factor_data.columns))\n",
    "\n",
    "# 创建因子分析器实例，设置因子数量为4\n",
    "fa = FactorAnalyzer(n_factors=4, rotation='varimax')\n",
    "\n",
    "# 拟合数据\n",
    "fa.fit(factor_data)\n",
    "\n",
    "# 获取因子载荷系数矩阵\n",
    "loadings = fa.loadings_\n",
    "\n",
    "# 确保因子载荷矩阵的行数与原始数据的列数相同\n",
    "assert loadings.shape[0] == factor_data.shape[\n",
    "    1], \"Number of rows in loadings matrix does not match number of columns in data.\"\n",
    "\n",
    "# 将因子载荷系数矩阵转换为DataFrame\n",
    "loadings_df = pd.DataFrame(loadings, columns=[f'Factor_{i + 1}' for i in range(4)], index=factor_data.columns)\n",
    "\n",
    "# 输出因子载荷系数DataFrame\n",
    "print(\"Factor Loadings:\")\n",
    "print(loadings_df)\n",
    "\n",
    "# 获取公因子方差（Communalities）\n",
    "communalities = fa.get_communalities()\n",
    "\n",
    "# 确保公因子方差的长度与原始数据的列数相同\n",
    "assert communalities.shape[0] == factor_data.shape[\n",
    "    1], \"Length of communalities does not match number of columns in data.\"\n",
    "\n",
    "# 将公因子方差转换为DataFrame\n",
    "communalities_df = pd.DataFrame({'Communalities': communalities}, index=factor_data.columns)\n",
    "\n",
    "# 输出公因子方差DataFrame\n",
    "print(\"\\nCommunalities:\")\n",
    "print(communalities_df)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-08-14T10:32:04.437417300Z",
     "start_time": "2024-08-14T10:32:04.390784700Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "旋转前的统计信息:\n",
      "             特征根  旋转前方差解释率%    旋转前方差累积%\n",
      "因子编号1   5.830391  44.849159   44.849159\n",
      "因子编号2   1.402716  10.790124   55.639283\n",
      "因子编号3   1.226820   9.437079   65.076363\n",
      "因子编号4   1.041829   8.014072   73.090434\n",
      "因子编号5   0.521196   4.009198   77.099633\n",
      "因子编号6   0.495731   3.813316   80.912949\n",
      "因子编号7   0.467668   3.597444   84.510393\n",
      "因子编号8   0.395330   3.041003   87.551395\n",
      "因子编号9   0.388279   2.986761   90.538156\n",
      "因子编号10  0.329781   2.536778   93.074934\n",
      "因子编号11  0.309407   2.380055   95.454989\n",
      "因子编号12  0.305444   2.349570   97.804559\n",
      "因子编号13  0.285407   2.195441  100.000000\n",
      "\n",
      "旋转后的统计信息:\n",
      "              特征根  旋转后方差解释率%    旋转后方差累积%\n",
      "因子编号1   14.503609  66.233317   66.233317\n",
      "因子编号2    2.739267  12.509351   78.742668\n",
      "因子编号3    2.387341  10.902219   89.644887\n",
      "因子编号4    1.952660   8.917168   98.562055\n",
      "因子编号5    0.081827   0.373677   98.935732\n",
      "因子编号6    0.075217   0.343493   99.279224\n",
      "因子编号7    0.059293   0.270774   99.549998\n",
      "因子编号8    0.037154   0.169670   99.719668\n",
      "因子编号9    0.032048   0.146354   99.866022\n",
      "因子编号10   0.020288   0.092651   99.958673\n",
      "因子编号11   0.008476   0.038707   99.997380\n",
      "因子编号12   0.000574   0.002620  100.000000\n",
      "因子编号13   0.000000   0.000000  100.000000\n"
     ]
    }
   ],
   "source": [
    "#方差解释率\n",
    "\n",
    "\n",
    "from factor_analyzer import FactorAnalyzer\n",
    "\n",
    "# 假设 'newcleaned_Shortvideo_data' 是你的DataFrame\n",
    "data = newcleaned_Shortvideo_data.select_dtypes(include=[np.number])\n",
    "data = data.dropna().reset_index(drop=True)\n",
    "if data.ndim != 2:\n",
    "    raise ValueError(\"Input data must be 2-dimensional.\")\n",
    "# 创建因子分析器实例，设置因子数量为13，不进行旋转\n",
    "fa = FactorAnalyzer(n_factors=13, rotation=None)\n",
    "fa.fit(data)\n",
    "# 获取特征根并计算方差解释率和累积方差\n",
    "eigenvalues, _ = fa.get_eigenvalues()\n",
    "proportion_variance = eigenvalues / eigenvalues.sum()\n",
    "cumulative_variance = np.cumsum(proportion_variance)\n",
    "# 只保留前4个特征根用于后续计算\n",
    "eigenvalues = eigenvalues[:13]\n",
    "# 计算旋转前的统计量\n",
    "stats_pre_rotated = pd.DataFrame({\n",
    "    '特征根': eigenvalues,\n",
    "    '旋转前方差解释率%': proportion_variance[:13] * 100,\n",
    "    '旋转前方差累积%': cumulative_variance[:13] * 100\n",
    "}, index=['因子编号{}'.format(i + 1) for i in range(len(eigenvalues))])\n",
    "# 输出旋转前的统计信息\n",
    "print(\"旋转前的统计信息:\")\n",
    "print(stats_pre_rotated)\n",
    "# 创建新的因子分析器实例，设置因子数量为4，使用最大方差法旋转\n",
    "fa_rotated = FactorAnalyzer(n_factors=13, rotation='varimax')\n",
    "fa_rotated.fit(data)\n",
    "# 扩展eigenvalues维度以匹配载荷矩阵的形状\n",
    "sqrt_eigenvalues_matrix = np.diag(np.sqrt(eigenvalues))\n",
    "# 进行矩阵运算以计算旋转后的特征根\n",
    "loadings_matrix_product = fa_rotated.loadings_.T @ fa_rotated.loadings_\n",
    "eigenvalues_rotated = sqrt_eigenvalues_matrix @ loadings_matrix_product @ sqrt_eigenvalues_matrix\n",
    "eigenvalues_rotated = np.diag(eigenvalues_rotated)\n",
    "# 计算旋转后的方差解释率和累积方差\n",
    "proportion_variance_rotated = eigenvalues_rotated / eigenvalues_rotated.sum()\n",
    "cumulative_variance_rotated = np.cumsum(proportion_variance_rotated)\n",
    "# 计算旋转后的统计量\n",
    "stats_post_rotated = pd.DataFrame({\n",
    "    '特征根': eigenvalues_rotated,\n",
    "    '旋转后方差解释率%': proportion_variance_rotated * 100,\n",
    "    '旋转后方差累积%': cumulative_variance_rotated * 100\n",
    "}, index=['因子编号{}'.format(i + 1) for i in range(len(eigenvalues_rotated))])\n",
    "# 输出旋转后的统计信息\n",
    "print(\"\\n旋转后的统计信息:\")\n",
    "print(stats_post_rotated)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-08-14T10:32:09.392714500Z",
     "start_time": "2024-08-14T10:32:09.315751200Z"
    }
   }
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5、描述性统计分析\n",
    "### 5.1样本人口统计学特征描述"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-08-14T11:15:29.345908400Z",
     "start_time": "2024-08-14T11:15:29.321867700Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "     Q1  Q2  Q3  Q4  Q5.1  Q5.2  Q5.3  Q5.4  Q5.5  Q5.6  Q6  Q7  Q8  \\\n0     1   2   3   5     1     1     1     0     0     0   1   1   3   \n1     2   3   4   4     0     1     0     0     0     0   3   3   6   \n2     1   3   4   3     0     0     0     0     1     0   4   1   4   \n3     2   5   5   4     0     0     1     0     0     0   4   3   5   \n4     1   2   1   2     0     0     1     0     1     0   1   3   3   \n..   ..  ..  ..  ..   ...   ...   ...   ...   ...   ...  ..  ..  ..   \n233   1   2   3   3     0     0     0     0     1     0   2   3   1   \n234   2   3   4   4     0     1     0     0     0     0   1   2   6   \n235   2   2   2   3     1     0     0     0     0     0   1   3   2   \n236   2   5   5   4     0     0     0     0     0     1   3   2   2   \n237   1   4   3   5     0     0     1     0     0     0   2   1   6   \n\n     Unnamed: 13  Unnamed: 14  Unnamed: 15  \n0              0            0            0  \n1              0            0            0  \n2              0            0            0  \n3              0            0            0  \n4              0            0            0  \n..           ...          ...          ...  \n233            0            0            0  \n234            0            0            0  \n235            0            0            0  \n236            0            0            0  \n237            0            0            0  \n\n[238 rows x 16 columns]",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Q1</th>\n      <th>Q2</th>\n      <th>Q3</th>\n      <th>Q4</th>\n      <th>Q5.1</th>\n      <th>Q5.2</th>\n      <th>Q5.3</th>\n      <th>Q5.4</th>\n      <th>Q5.5</th>\n      <th>Q5.6</th>\n      <th>Q6</th>\n      <th>Q7</th>\n      <th>Q8</th>\n      <th>Unnamed: 13</th>\n      <th>Unnamed: 14</th>\n      <th>Unnamed: 15</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1</td>\n      <td>2</td>\n      <td>3</td>\n      <td>5</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>3</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2</td>\n      <td>3</td>\n      <td>4</td>\n      <td>4</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>3</td>\n      <td>3</td>\n      <td>6</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>1</td>\n      <td>3</td>\n      <td>4</td>\n      <td>3</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>4</td>\n      <td>1</td>\n      <td>4</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>2</td>\n      <td>5</td>\n      <td>5</td>\n      <td>4</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>4</td>\n      <td>3</td>\n      <td>5</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>1</td>\n      <td>2</td>\n      <td>1</td>\n      <td>2</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>3</td>\n      <td>3</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>233</th>\n      <td>1</td>\n      <td>2</td>\n      <td>3</td>\n      <td>3</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>2</td>\n      <td>3</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>234</th>\n      <td>2</td>\n      <td>3</td>\n      <td>4</td>\n      <td>4</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>2</td>\n      <td>6</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>235</th>\n      <td>2</td>\n      <td>2</td>\n      <td>2</td>\n      <td>3</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>3</td>\n      <td>2</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>236</th>\n      <td>2</td>\n      <td>5</td>\n      <td>5</td>\n      <td>4</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>3</td>\n      <td>2</td>\n      <td>2</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>237</th>\n      <td>1</td>\n      <td>4</td>\n      <td>3</td>\n      <td>5</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>2</td>\n      <td>1</td>\n      <td>6</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n<p>238 rows × 16 columns</p>\n</div>"
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Q1original_Shortvideo_data = pd.read_csv(\"Q1-8Shortvideo_data.csv\")\n",
    "Q1original_Shortvideo_data.fillna(0, inplace=True)\n",
    "Q1original_Shortvideo_data = Q1original_Shortvideo_data.astype(float).astype(int)\n",
    "Q1original_Shortvideo_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 238 entries, 0 to 237\n",
      "Data columns (total 16 columns):\n",
      " #   Column       Non-Null Count  Dtype\n",
      "---  ------       --------------  -----\n",
      " 0   Q1           238 non-null    int32\n",
      " 1   Q2           238 non-null    int32\n",
      " 2   Q3           238 non-null    int32\n",
      " 3   Q4           238 non-null    int32\n",
      " 4   Q5.1         238 non-null    int32\n",
      " 5   Q5.2         238 non-null    int32\n",
      " 6   Q5.3         238 non-null    int32\n",
      " 7   Q5.4         238 non-null    int32\n",
      " 8   Q5.5         238 non-null    int32\n",
      " 9   Q5.6         238 non-null    int32\n",
      " 10  Q6           238 non-null    int32\n",
      " 11  Q7           238 non-null    int32\n",
      " 12  Q8           238 non-null    int32\n",
      " 13  Unnamed: 13  238 non-null    int32\n",
      " 14  Unnamed: 14  238 non-null    int32\n",
      " 15  Unnamed: 15  238 non-null    int32\n",
      "dtypes: int32(16)\n",
      "memory usage: 15.0 KB\n"
     ]
    }
   ],
   "source": [
    "Q1original_Shortvideo_data.info()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-08-14T11:16:04.710616100Z",
     "start_time": "2024-08-14T11:16:04.681115200Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Column: Q1\n",
      "Frequency:\n",
      " Q1\n",
      "1    124\n",
      "2    114\n",
      "3      0\n",
      "4      0\n",
      "5      0\n",
      "6      0\n",
      "Name: count, dtype: int64\n",
      "Percentage:\n",
      " Q1\n",
      "1    52.1\n",
      "2    47.9\n",
      "Name: proportion, dtype: float64\n",
      "Cumulative Percentage:\n",
      " Q1\n",
      "1     52.1\n",
      "2    100.0\n",
      "Name: proportion, dtype: float64\n",
      "\n",
      "---\n",
      "\n",
      "Column: Q2\n",
      "Frequency:\n",
      " Q2\n",
      "1    29\n",
      "2    70\n",
      "3    55\n",
      "4    47\n",
      "5    37\n",
      "6     0\n",
      "Name: count, dtype: int64\n",
      "Percentage:\n",
      " Q2\n",
      "2    29.41\n",
      "3    23.11\n",
      "4    19.75\n",
      "5    15.55\n",
      "1    12.18\n",
      "Name: proportion, dtype: float64\n",
      "Cumulative Percentage:\n",
      " Q2\n",
      "2     29.41\n",
      "3     52.52\n",
      "4     72.27\n",
      "5     87.82\n",
      "1    100.00\n",
      "Name: proportion, dtype: float64\n",
      "\n",
      "---\n",
      "\n",
      "Column: Q3\n",
      "Frequency:\n",
      " Q3\n",
      "1    54\n",
      "2    85\n",
      "3    52\n",
      "4    10\n",
      "5    37\n",
      "6     0\n",
      "Name: count, dtype: int64\n",
      "Percentage:\n",
      " Q3\n",
      "2    35.71\n",
      "1    22.69\n",
      "3    21.85\n",
      "5    15.55\n",
      "4     4.20\n",
      "Name: proportion, dtype: float64\n",
      "Cumulative Percentage:\n",
      " Q3\n",
      "2     35.71\n",
      "1     58.40\n",
      "3     80.25\n",
      "5     95.80\n",
      "4    100.00\n",
      "Name: proportion, dtype: float64\n",
      "\n",
      "---\n",
      "\n",
      "Column: Q4\n",
      "Frequency:\n",
      " Q4\n",
      "1    28\n",
      "2    26\n",
      "3    48\n",
      "4    63\n",
      "5    73\n",
      "6     0\n",
      "Name: count, dtype: int64\n",
      "Percentage:\n",
      " Q4\n",
      "5    30.67\n",
      "4    26.47\n",
      "3    20.17\n",
      "1    11.76\n",
      "2    10.92\n",
      "Name: proportion, dtype: float64\n",
      "Cumulative Percentage:\n",
      " Q4\n",
      "5    30.67\n",
      "4    57.14\n",
      "3    77.31\n",
      "1    89.07\n",
      "2    99.99\n",
      "Name: proportion, dtype: float64\n",
      "\n",
      "---\n",
      "\n",
      "Column: Q5.1\n",
      "Frequency:\n",
      " Q5.1\n",
      "1    79\n",
      "2     0\n",
      "3     0\n",
      "4     0\n",
      "5     0\n",
      "6     0\n",
      "Name: count, dtype: int64\n",
      "Percentage:\n",
      " Q5.1\n",
      "0    66.81\n",
      "1    33.19\n",
      "Name: proportion, dtype: float64\n",
      "Cumulative Percentage:\n",
      " Q5.1\n",
      "0     66.81\n",
      "1    100.00\n",
      "Name: proportion, dtype: float64\n",
      "\n",
      "---\n",
      "\n",
      "Column: Q5.2\n",
      "Frequency:\n",
      " Q5.2\n",
      "1    85\n",
      "2     0\n",
      "3     0\n",
      "4     0\n",
      "5     0\n",
      "6     0\n",
      "Name: count, dtype: int64\n",
      "Percentage:\n",
      " Q5.2\n",
      "0    64.29\n",
      "1    35.71\n",
      "Name: proportion, dtype: float64\n",
      "Cumulative Percentage:\n",
      " Q5.2\n",
      "0     64.29\n",
      "1    100.00\n",
      "Name: proportion, dtype: float64\n",
      "\n",
      "---\n",
      "\n",
      "Column: Q5.3\n",
      "Frequency:\n",
      " Q5.3\n",
      "1    74\n",
      "2     0\n",
      "3     0\n",
      "4     0\n",
      "5     0\n",
      "6     0\n",
      "Name: count, dtype: int64\n",
      "Percentage:\n",
      " Q5.3\n",
      "0    68.91\n",
      "1    31.09\n",
      "Name: proportion, dtype: float64\n",
      "Cumulative Percentage:\n",
      " Q5.3\n",
      "0     68.91\n",
      "1    100.00\n",
      "Name: proportion, dtype: float64\n",
      "\n",
      "---\n",
      "\n",
      "Column: Q5.4\n",
      "Frequency:\n",
      " Q5.4\n",
      "1    72\n",
      "2     0\n",
      "3     0\n",
      "4     0\n",
      "5     0\n",
      "6     0\n",
      "Name: count, dtype: int64\n",
      "Percentage:\n",
      " Q5.4\n",
      "0    69.75\n",
      "1    30.25\n",
      "Name: proportion, dtype: float64\n",
      "Cumulative Percentage:\n",
      " Q5.4\n",
      "0     69.75\n",
      "1    100.00\n",
      "Name: proportion, dtype: float64\n",
      "\n",
      "---\n",
      "\n",
      "Column: Q5.5\n",
      "Frequency:\n",
      " Q5.5\n",
      "1    80\n",
      "2     0\n",
      "3     0\n",
      "4     0\n",
      "5     0\n",
      "6     0\n",
      "Name: count, dtype: int64\n",
      "Percentage:\n",
      " Q5.5\n",
      "0    66.39\n",
      "1    33.61\n",
      "Name: proportion, dtype: float64\n",
      "Cumulative Percentage:\n",
      " Q5.5\n",
      "0     66.39\n",
      "1    100.00\n",
      "Name: proportion, dtype: float64\n",
      "\n",
      "---\n",
      "\n",
      "Column: Q5.6\n",
      "Frequency:\n",
      " Q5.6\n",
      "1    62\n",
      "2     0\n",
      "3     0\n",
      "4     0\n",
      "5     0\n",
      "6     0\n",
      "Name: count, dtype: int64\n",
      "Percentage:\n",
      " Q5.6\n",
      "0    73.95\n",
      "1    26.05\n",
      "Name: proportion, dtype: float64\n",
      "Cumulative Percentage:\n",
      " Q5.6\n",
      "0     73.95\n",
      "1    100.00\n",
      "Name: proportion, dtype: float64\n",
      "\n",
      "---\n",
      "\n",
      "Column: Q6\n",
      "Frequency:\n",
      " Q6\n",
      "1    71\n",
      "2    55\n",
      "3    18\n",
      "4    94\n",
      "5     0\n",
      "6     0\n",
      "Name: count, dtype: int64\n",
      "Percentage:\n",
      " Q6\n",
      "4    39.50\n",
      "1    29.83\n",
      "2    23.11\n",
      "3     7.56\n",
      "Name: proportion, dtype: float64\n",
      "Cumulative Percentage:\n",
      " Q6\n",
      "4     39.50\n",
      "1     69.33\n",
      "2     92.44\n",
      "3    100.00\n",
      "Name: proportion, dtype: float64\n",
      "\n",
      "---\n",
      "\n",
      "Column: Q7\n",
      "Frequency:\n",
      " Q7\n",
      "1    84\n",
      "2    63\n",
      "3    91\n",
      "4     0\n",
      "5     0\n",
      "6     0\n",
      "Name: count, dtype: int64\n",
      "Percentage:\n",
      " Q7\n",
      "3    38.24\n",
      "1    35.29\n",
      "2    26.47\n",
      "Name: proportion, dtype: float64\n",
      "Cumulative Percentage:\n",
      " Q7\n",
      "3     38.24\n",
      "1     73.53\n",
      "2    100.00\n",
      "Name: proportion, dtype: float64\n",
      "\n",
      "---\n",
      "\n",
      "Column: Q8\n",
      "Frequency:\n",
      " Q8\n",
      "1    19\n",
      "2    53\n",
      "3    46\n",
      "4    33\n",
      "5    39\n",
      "6    48\n",
      "Name: count, dtype: int64\n",
      "Percentage:\n",
      " Q8\n",
      "2    22.27\n",
      "6    20.17\n",
      "3    19.33\n",
      "5    16.39\n",
      "4    13.87\n",
      "1     7.98\n",
      "Name: proportion, dtype: float64\n",
      "Cumulative Percentage:\n",
      " Q8\n",
      "2     22.27\n",
      "6     42.44\n",
      "3     61.77\n",
      "5     78.16\n",
      "4     92.03\n",
      "1    100.01\n",
      "Name: proportion, dtype: float64\n",
      "\n",
      "---\n",
      "\n",
      "Column: Unnamed: 13\n",
      "Frequency:\n",
      " Unnamed: 13\n",
      "1    0\n",
      "2    0\n",
      "3    0\n",
      "4    0\n",
      "5    0\n",
      "6    0\n",
      "Name: count, dtype: int64\n",
      "Percentage:\n",
      " Unnamed: 13\n",
      "0    100.0\n",
      "Name: proportion, dtype: float64\n",
      "Cumulative Percentage:\n",
      " Unnamed: 13\n",
      "0    100.0\n",
      "Name: proportion, dtype: float64\n",
      "\n",
      "---\n",
      "\n",
      "Column: Unnamed: 14\n",
      "Frequency:\n",
      " Unnamed: 14\n",
      "1    0\n",
      "2    0\n",
      "3    0\n",
      "4    0\n",
      "5    0\n",
      "6    0\n",
      "Name: count, dtype: int64\n",
      "Percentage:\n",
      " Unnamed: 14\n",
      "0    100.0\n",
      "Name: proportion, dtype: float64\n",
      "Cumulative Percentage:\n",
      " Unnamed: 14\n",
      "0    100.0\n",
      "Name: proportion, dtype: float64\n",
      "\n",
      "---\n",
      "\n",
      "Column: Unnamed: 15\n",
      "Frequency:\n",
      " Unnamed: 15\n",
      "1    0\n",
      "2    0\n",
      "3    0\n",
      "4    0\n",
      "5    0\n",
      "6    0\n",
      "Name: count, dtype: int64\n",
      "Percentage:\n",
      " Unnamed: 15\n",
      "0    100.0\n",
      "Name: proportion, dtype: float64\n",
      "Cumulative Percentage:\n",
      " Unnamed: 15\n",
      "0    100.0\n",
      "Name: proportion, dtype: float64\n",
      "\n",
      "---\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# 创建一个字典来存储结果\n",
    "results = {}\n",
    "\n",
    "# 对每一列进行统计\n",
    "for col in Q1original_Shortvideo_data.columns:\n",
    "    # 计算频次\n",
    "    freq = Q1original_Shortvideo_data[col].value_counts(dropna=False).reindex(range(1, 7), fill_value=0)\n",
    "\n",
    "    # 计算百分比\n",
    "    percent = Q1original_Shortvideo_data[col].value_counts(normalize=True, dropna=False) * 100\n",
    "    percent = percent.round(2)\n",
    "    # 计算累计百分比\n",
    "    cum_percent = percent.cumsum().round(2)\n",
    "\n",
    "    # 将结果存入字典\n",
    "    results[col] = {'Frequency': freq, 'Percentage': percent, 'Cumulative Percentage': cum_percent}\n",
    "\n",
    "# 输出结果\n",
    "for col, data in results.items():\n",
    "    print(f\"Column: {col}\")\n",
    "    print(\"Frequency:\\n\", data['Frequency'])\n",
    "    print(\"Percentage:\\n\", data['Percentage'])\n",
    "    print(\"Cumulative Percentage:\\n\", data['Cumulative Percentage'])\n",
    "    print(\"\\n---\\n\")"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-08-14T10:24:08.867675800Z",
     "start_time": "2024-08-14T10:24:08.681898300Z"
    }
   }
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "从头部的10行数据来看，数据符合“每个变量为一列，每个观察值为一行，每种类型的观察单位为一个表格”，因此不存在结构性问题。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-08-14T10:24:08.899577700Z",
     "start_time": "2024-08-14T10:24:08.718929700Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "     Hobby  Fit  Customized  Need  Curiosity  Feel  Release_pressure  \\\n0        2    1           1     2          1     1                 1   \n1        1    1           2     1          1     2                 1   \n2        1    2           2     1          2     2                 1   \n3        3    3           5     4          5     5                 3   \n4        3    3           4     3          3     3                 4   \n..     ...  ...         ...   ...        ...   ...               ...   \n233      5    5           3     3          4     3                 3   \n234      3    5           4     4          3     3                 5   \n235      2    1           1     2          1     1                 1   \n236      3    4           5     4          3     3                 5   \n237      4    4           4     3          3     5                 3   \n\n     understand  Decision  Risk  Possibility  Worth  Recommend  \n0             2         1     2            2      2          2  \n1             2         2     2            2      2          1  \n2             2         1     1            2      1          2  \n3             5         4     3            5      3          4  \n4             5         5     5            3      3          4  \n..          ...       ...   ...          ...    ...        ...  \n233           3         3     4            5      3          5  \n234           5         3     5            4      5          3  \n235           1         1     2            2      1          2  \n236           4         4     4            3      4          4  \n237           3         3     3            4      5          5  \n\n[238 rows x 13 columns]",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Hobby</th>\n      <th>Fit</th>\n      <th>Customized</th>\n      <th>Need</th>\n      <th>Curiosity</th>\n      <th>Feel</th>\n      <th>Release_pressure</th>\n      <th>understand</th>\n      <th>Decision</th>\n      <th>Risk</th>\n      <th>Possibility</th>\n      <th>Worth</th>\n      <th>Recommend</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>2</td>\n      <td>1</td>\n      <td>1</td>\n      <td>2</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>2</td>\n      <td>1</td>\n      <td>2</td>\n      <td>2</td>\n      <td>2</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1</td>\n      <td>1</td>\n      <td>2</td>\n      <td>1</td>\n      <td>1</td>\n      <td>2</td>\n      <td>1</td>\n      <td>2</td>\n      <td>2</td>\n      <td>2</td>\n      <td>2</td>\n      <td>2</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>1</td>\n      <td>2</td>\n      <td>2</td>\n      <td>1</td>\n      <td>2</td>\n      <td>2</td>\n      <td>1</td>\n      <td>2</td>\n      <td>1</td>\n      <td>1</td>\n      <td>2</td>\n      <td>1</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>3</td>\n      <td>3</td>\n      <td>5</td>\n      <td>4</td>\n      <td>5</td>\n      <td>5</td>\n      <td>3</td>\n      <td>5</td>\n      <td>4</td>\n      <td>3</td>\n      <td>5</td>\n      <td>3</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>3</td>\n      <td>3</td>\n      <td>4</td>\n      <td>3</td>\n      <td>3</td>\n      <td>3</td>\n      <td>4</td>\n      <td>5</td>\n      <td>5</td>\n      <td>5</td>\n      <td>3</td>\n      <td>3</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>233</th>\n      <td>5</td>\n      <td>5</td>\n      <td>3</td>\n      <td>3</td>\n      <td>4</td>\n      <td>3</td>\n      <td>3</td>\n      <td>3</td>\n      <td>3</td>\n      <td>4</td>\n      <td>5</td>\n      <td>3</td>\n      <td>5</td>\n    </tr>\n    <tr>\n      <th>234</th>\n      <td>3</td>\n      <td>5</td>\n      <td>4</td>\n      <td>4</td>\n      <td>3</td>\n      <td>3</td>\n      <td>5</td>\n      <td>5</td>\n      <td>3</td>\n      <td>5</td>\n      <td>4</td>\n      <td>5</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>235</th>\n      <td>2</td>\n      <td>1</td>\n      <td>1</td>\n      <td>2</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>2</td>\n      <td>2</td>\n      <td>1</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>236</th>\n      <td>3</td>\n      <td>4</td>\n      <td>5</td>\n      <td>4</td>\n      <td>3</td>\n      <td>3</td>\n      <td>5</td>\n      <td>4</td>\n      <td>4</td>\n      <td>4</td>\n      <td>3</td>\n      <td>4</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>237</th>\n      <td>4</td>\n      <td>4</td>\n      <td>4</td>\n      <td>3</td>\n      <td>3</td>\n      <td>5</td>\n      <td>3</td>\n      <td>3</td>\n      <td>3</td>\n      <td>3</td>\n      <td>4</td>\n      <td>5</td>\n      <td>5</td>\n    </tr>\n  </tbody>\n</table>\n<p>238 rows × 13 columns</p>\n</div>"
     },
     "execution_count": 204,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "newcleaned_Shortvideo_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "outputs": [],
   "source": [
    "# 解决matplotlib中，不能显示汉字的问题\n",
    "# Windows 下\n",
    "plt.rcParams['font.family'] = 'SimHei'  # 设置字体\n",
    "plt.rcParams['axes.unicode_minus'] = False  # 正常显示字符\n",
    "# Mac OS  下\n",
    "# plt.rcParams['font.family'] = 'Heiti TC'  # 设置字体\n",
    "# plt.rcParams['axes.unicode_minus'] = False  # 正常显示字符"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-08-14T10:24:08.921739800Z",
     "start_time": "2024-08-14T10:24:08.721946Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 1000x600 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# 定义态度等级的标签\n",
    "attitude_labels = {\n",
    "    1: '完全不同意',\n",
    "    2: '不同意',\n",
    "    3: '一般',\n",
    "    4: '同意',\n",
    "    5: '完全同意'\n",
    "}\n",
    "\n",
    "# 映射原始数据中的数值到这些标签\n",
    "attitude_order = list(attitude_labels.values())\n",
    "\n",
    "# 加载数据\n",
    "df = pd.read_excel('newcleaned_Shortvideo_data.xlsx', sheet_name='Sheet1')\n",
    "\n",
    "# 将数值映射到标签\n",
    "df['Hobby_label'] = df['Hobby'].map(attitude_labels)\n",
    "\n",
    "# 计算每种态度的频率，并转换成百分比\n",
    "attitude_counts = df['Hobby_label'].value_counts(normalize=True) * 100\n",
    "\n",
    "# 绘制条形图\n",
    "plt.figure(figsize=(10, 6))\n",
    "ax = attitude_counts.reindex(index=attitude_order).plot(kind='bar', color='skyblue')\n",
    "plt.title('短视频生活场景推送的产品和视频符合我的个人喜好')\n",
    "plt.xlabel('态度等级')\n",
    "plt.ylabel('百分比 (%)')\n",
    "plt.xticks(rotation=0)\n",
    "\n",
    "# 在每个柱形上添加百分比标签\n",
    "for p in ax.patches:\n",
    "    ax.annotate(f'{p.get_height():.1f}%',\n",
    "                (p.get_x() + p.get_width() / 2., p.get_height()),\n",
    "                ha='center', va='center',\n",
    "                xytext=(0, 10), textcoords='offset points')\n",
    "\n",
    "plt.gca().yaxis.grid(True)  # 添加网格线\n",
    "plt.tight_layout()  # 自动调整子图参数，使之填充整个图像区域\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-08-14T10:24:09.116162700Z",
     "start_time": "2024-08-14T10:24:08.722971400Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 1000x600 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 定义态度等级的标签\n",
    "attitude_labels = {\n",
    "    1: '完全不同意',\n",
    "    2: '不同意',\n",
    "    3: '一般',\n",
    "    4: '同意',\n",
    "    5: '完全同意'\n",
    "}\n",
    "\n",
    "# 映射原始数据中的数值到这些标签\n",
    "attitude_order = list(attitude_labels.values())\n",
    "\n",
    "# 加载数据\n",
    "df = pd.read_excel('newcleaned_Shortvideo_data.xlsx', sheet_name='Sheet1')\n",
    "\n",
    "# 将数值映射到标签\n",
    "df['Release_pressure'] = df['Release_pressure'].map(attitude_labels)\n",
    "\n",
    "# 计算每种态度的频率，并转换成百分比\n",
    "attitude_counts = df['Release_pressure'].value_counts(normalize=True) * 100\n",
    "\n",
    "# 绘制条形图\n",
    "plt.figure(figsize=(10, 6))\n",
    "ax = attitude_counts.reindex(index=attitude_order).plot(kind='bar', color='skyblue')\n",
    "plt.title('在观看短视频生活场景过程中能释放我的压力')\n",
    "plt.xlabel('态度等级')\n",
    "plt.ylabel('百分比 (%)')\n",
    "plt.xticks(rotation=0)\n",
    "\n",
    "# 在每个柱形上添加百分比标签\n",
    "for p in ax.patches:\n",
    "    ax.annotate(f'{p.get_height():.1f}%',\n",
    "                (p.get_x() + p.get_width() / 2., p.get_height()),\n",
    "                ha='center', va='center',\n",
    "                xytext=(0, 10), textcoords='offset points')\n",
    "\n",
    "plt.gca().yaxis.grid(True)  # 添加网格线\n",
    "plt.tight_layout()  # 自动调整子图参数，使之填充整个图像区域\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 800x800 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# 定义态度等级的标签\n",
    "attitude_labels = {\n",
    "    1: '完全不同意',\n",
    "    2: '不同意',\n",
    "    3: '一般',\n",
    "    4: '同意',\n",
    "    5: '完全同意'\n",
    "}\n",
    "\n",
    "# 加载数据\n",
    "df = pd.read_excel('newcleaned_Shortvideo_data.xlsx', sheet_name='Sheet1')\n",
    "\n",
    "# 计算'Hobby'列中每个态度等级的频率\n",
    "attitude_counts = df['Decision'].value_counts()\n",
    "\n",
    "# 将数值映射到标签\n",
    "labels = [attitude_labels[i] for i in attitude_counts.index]\n",
    "\n",
    "# 创建饼图\n",
    "plt.figure(figsize=(8, 8))\n",
    "plt.pie(attitude_counts, labels=labels, autopct='%1.1f%%', startangle=140)\n",
    "plt.title('通过观看短视频生活场景内容，对我购买决策有帮助')\n",
    "plt.axis('equal')  # Equal aspect ratio ensures that pie is drawn as a circle.\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [],
   "metadata": {
    "collapsed": false
   }
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
